| Wang Xiaohua (王小华)* **,Ma Pin**,Wang Hua* **,Li Li* **.[J].高技术通讯(英文),2020,26(4):383~389 |
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| Dynamic A* path finding algorithm and 3D lidar based obstacle avoidance strategy for autonomous vehicles |
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| DOI：10.3772/j.issn.1006-6748.2020.04.005 |
| 中文关键词: |
| 英文关键词: autonomous navigation, local obstacle avoidance, dynamic A* path finding algorithm, point cloud processing, local obstacle map |
| 基金项目: |
| Author Name | Affiliation | | Wang Xiaohua (王小华)* ** | (*Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200444, P.R.China)
(**School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China) | | Ma Pin** | (**School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China) | | Wang Hua* ** | (*Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200444, P.R.China)
(**School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China) | | Li Li* ** | (*Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200444, P.R.China)
(**School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| This paper presents a novel dynamic A* path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle. 3D point cloud data is collected and analyzed in real time. Local obstacles are detected online and a 2D local obstacle grid map is constructed at 10Hz/s. The A* path finding algorithm is employed to generate a local path in this local obstacle grid map by considering both the target position and obstacles. The vehicle avoids obstacles under the guidance of the generated local path. Experiment results have shown the effectiveness of the obstacle avoidance navigation algorithm proposed. |
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